• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.97-110
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• 2021

Fog can cause large-scale human and economic damages, including traffic systems and agriculture. So, Korea Meteorological Administration is operating about 290 visibility meters to improve the observation level of fog. However, it is still insufficient to detect very localized fog. In this study, high-resolution grid-type visibility data were retrieved from irregularly distributed visibility data across the country. To this end, three objective analysis techniques (Inverse Distance Weighting (IDW), Ordinary Kriging (OK) and Universal Kriging (UK)) were used. To find the best method and parameters, sensitivity test was performed for the effective radius, power parameter and variogram model that affect the level of objective analysis. Also, the effect of data distribution characteristics (level of normality) on the performance level of objective analysis was evaluated. IDW showed a relatively high level of objective analysis in terms of bias, RMSE and correlation, and the performance is inversely proportional to the effective radius and power parameter. However, the two Krigings showed relatively low level of objective analysis, in particular, greatly weakened the variability of the variables, although the level of output was different depending on the variogram model used. As the level of objective analysis is greatly influenced by the distribution characteristics of data, power, and models used, care should be taken when selecting objective analysis techniques and parameters.

• Journal of computational fluids engineering
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• v.9 no.1
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• pp.34-40
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• 2004

Recently with growth in the capability of super computers and Parallel computers, shape design optimization is becoming easible for real problems. Also, Computational Fluid Dynamics(CFD) techniques have been improved for higher reliability and higher accuracy. In the shape design optimization, analysis solvers and optimization schemes are essential. In this work, the Roe's 2nd-order Upwind TVD scheme and DADI time march with multigrid were used for the flow solution with the Euler equation and FDM(Finite Differenciation Method), GA(Genetic Algorithm) and Kriging were used for the design optimization. Kriging were applied to 2-D airfoil design optimization and compared with FDM and GA's results. When Kriging is applied to the nonlinear problems, satisfactory results were obtained. From the result design optimization by Kriging method appeared as good as other methods.

• Geotechnical Engineering
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• v.13 no.1
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• pp.85-100
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• 1997

The heterogeneity of hydraulic conductivity of Metropolitan Waste Landfill is analized by using geostatistical methods and the contaminant transport analysis is performed by using heterogeneous hydraulic conductivity. The hydraulic conductivity data are obtained from laboratory pressurized permeability tests and the insitu, Slug test. Geostatistical methods used in this analysis are Ordinary Kriging and conditional simulation. It is concluded that the heterogeneities of hydraulic conductivity obtained from conditional simulation are greater than those from Ordinary Kriging analysis. The movement of the contaminant on the hydraulic conductivity with greater heterogeneity obtained from conditional simulation is faster than that observed in Ordinary Kriging analysis.

• Geomechanics and Engineering
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• v.30 no.2
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• pp.187-199
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• 2022

Kriging metamodel, as a flexible machine learning method for approximating deterministic analysis models of an engineering system, has been widely used for efficiently estimating slope reliability in recent years. However, the autocorrelation function (ACF), a key input to Kriging that affects the accuracy of reliability estimation, is usually selected based on empiricism. This paper proposes an adaption of the Kriging method, named as Genetic Algorithm optimized Whittle-Matérn Kriging (GAWMK), for addressing this issue. The non-classical two-parameter Whittle-Matérn (WM) function, which can represent different ACFs in the Matérn family by controlling a smoothness parameter, is adopted in GAWMK to avoid subjectively selecting ACFs. The genetic algorithm is used to optimize the WM model to adaptively select the optimal autocorrelation structure of the GAWMK model. Monte Carlo simulation is then performed based on GAWMK for a subsequent slope reliability analysis. Applications to one explicit analytical example and two slope examples are presented to illustrate and validate the proposed method. It is found that reliability results estimated by the Kriging models using randomly chosen ACFs might be biased. The proposed method performs reasonably well in slope reliability estimation.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.169.2-169.2
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• 2005

• Journal of Mechanical Science and Technology
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• v.16 no.5
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• pp.619-632
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• 2002

In recent study on design of experiments, the complicate metamodeling has been studied because defining exact model using computer simulation is expensive and time consuming. Thus, some designers often use approximate models, which express the relation between some inputs and outputs. In this paper, we review and compare the complicate metamodels, which are expressed by the interaction of various data through trying many physical experiments and running a computer simulation. The prediction model in this paper employs interpolation schemes known as ordinary kriging developed in the fields of spatial statistics and kriging in Design and Analysis of Computer Experiments (DACE) model. We will focus on describing the definitions, the prediction functions and the algorithms of two kriging methods, and assess the error measures of those by using some validation methods.

• Journal of the Korean Statistical Society
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• v.36 no.1
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• pp.57-76
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• 2007

Kriging is a nonparametric regression method used in geostatistics for estimating curves and surfaces for spatial data. It may come as a surprise that the Kriging estimator, normally derived as the best linear unbiased estimator, is also the solution of a particular variational problem. Thus, Kriging estimators can also be interpreted as generalized smoothing splines where the roughness penalty is determined by the covariance function of a spatial process. We build off the early work by Silverman (1982, 1984) and the analysis by Cox (1983, 1984), Messer (1991), Messer and Goldstein (1993) and others and develop an equivalent kernel interpretation of geostatistical estimators. Given this connection we show how a given covariance function influences the bias and variance of the Kriging estimate as well as the mean squared prediction error. Some specific asymptotic results are given in one dimension for Matern covariances that have as their limit cubic smoothing splines.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.145-153
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• 2015

This study aims to accurately estimate population distribution more specifically than administrative unites using a RK (Regression-Kriging) model. The RK model is the areal interpolation technique that involves linear regression and the Kriging model. In order to estimate a population’s distribution using a sample region, four different models were used, namely; a regression model, RK model, OK (Ordinary Kriging) model and CK (Co-Kriging) model. The results were then compared with each other. Evaluation of the accuracy and validity of evaluation analysis results were the basis RMSE (Root Mean Square Error), MAE (Mean Absolute Error), G statistic and correlation coefficient (ρ). In the sample regions, every statistic value of the RK model showed better results than other models. The results of this comparative study will be useful to estimate a population distribution of the metropolitan areas with high population density

• Composites Research
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• v.16 no.4
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• pp.36-43
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• 2003

In this research, the optimization of composite structures was performed using metamodels. The optimization of composite structures requires a lot of time when optimizing the result of the time-consuming analysis. Thus, metamodels are used to replace the time-consuming analysis with simple models. RSM, kriging and neural networks are widely used metamodels. RSM and kriging were used in this study. The ultimate failure load analysis of the composite structure was approximated by metamodels. The optimizations of the composite plate were performed to maximize ultimate failure load using genetic algorithm and metamodels.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2000.11a
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• pp.262-265
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• 2000

In order to estimate spatial physicochemical properties of the spring waters in the study area, spring waters at 57 sites were investigated for measuring ten items (temperature, pH, Eh, EC, TDS, DO, salinity, alkalinity, discharge rate, and surface elevation), To compare each component with one another, regression analysis was carried out. Kriging was used to estimate the spatial characteristics and continuity of data in the study area. To solve kriging equation, the semivariogram was calculated using geostatistical software GS$^{+}$(version 3.1). As a result of semivariogram analysis, the data of nine components but surface elevation could be assumed as stationary random function, and ordinary kriging method was used for making contour maps.s.